MinMaxLTTB

A Rust crate for downsampling timeseries data using the LTTB (Largest Triangle Three Buckets) and MinMaxLTTB algorithms.

The classic LTTB algorithm is implemented as described in the original paper Downsampling Time Series for Visual Representation. The MinMaxLTTB algorithm follows MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB.

The crate draws inspiration from other implementations of LTTB and its variants

• https://github.com/jeromefroe/lttb-rs
• https://github.com/predict-idlab/MinMaxLTTB
• https://github.com/predict-idlab/tsdownsample
• https://github.com/cpbotha/lttb-bench/

Variants

• Classic LTTB: Classic implementation of LTTB downsampling with bucket binning based on equal count of points
• Standard LTTB: Alternative implementation of LTTB downsampling with bucket binning based on equidistant x-range for all buckets
• MinMax LTTB: MinMax variant that preserves local minima and maxima and is more computationally efficient

The restriction on the input data is that points must represent a timeseries, with strictly monotonically increasing values of x.

Usage

Add to your Cargo.toml:

[dependencies]
minmaxlttb = "0.1"

use minmaxlttb::{Point, Lttb, LttbBuilder, LttbMethod, Binning};

// E.g., usage with convenience functions
let points = vec![
    Point::new(0.0, 1.0),
    Point::new(1.0, 2.0),
    Point::new(2.0, 3.0),
    Point::new(3.0, 4.0),
];

// Classic LTTB (equal-count buckets)
let classic = minmaxlttb::lttb(&points, 3, Binning::ByCount).unwrap();

// Standard LTTB (equal x-range buckets)
let standard = minmaxlttb::lttb(&points, 3, Binning::ByRange).unwrap();

// Advanced usage with builder pattern, e.g., using MinMax LTTB with ratio=3
let lttb = LttbBuilder::new()
    .threshold(3)
    .method(LttbMethod::MinMax)
    .ratio(3)
    .build();

let result = lttb.downsample(&points).unwrap();

// Reuse the same configuration for multiple datasets
let dataset1 = vec![Point::new(0.0, 10.0), Point::new(1.0, 20.0), Point::new(2.0, 30.0), Point::new(3.0, 40.0)];
let dataset2 = vec![Point::new(0.0, 30.0), Point::new(1.0, 40.0), Point::new(2.0, 50.0), Point::new(3.0, 60.0)];

let lttb = LttbBuilder::new()
    .threshold(3)
    .method(LttbMethod::Classic)
    .build();

let result1 = lttb.downsample(&dataset1).unwrap();
let result2 = lttb.downsample(&dataset2).unwrap();

Result of MinMaxLTTB on 5.000-point timeseries

Below images show the effects of downsampling a 5.000-point timeseries with MinMaxLTTB with different thresholds.

The plots have been exported from the timeseries example using the --method minmax argument

cargo run -p example_timeseries -- --method minmax

Examples

Check the examples directory for examples that generate plots using the plotly-rs crate and are rendered in your system's default browser:

• power: example of downsampling a real-world power consumption dataset with MinMaxLTTB
• timeseries: example of downsampling a synthetic dataset with classic LTTB
• minmax_vs_classic: comparison of LTTB and MinMaxLTTB downsampling
• minmaxlttb_analysis: a visual analysis of the point selections and bucket/partition division for the MinMaxLTTB algorithm

Timeseries example

cargo run -p example_timeseries --release

Powerdata series example

cargo run -p example_power --release

The plot below is a zoom-in on a region of the plot to emphasize the differences

LTTB vs MinMaxLttb example

cargo run -p example_minmax_vs_classic --release

MinMaxLttb analysis example

This example generates a plot that illustrates how the MinMaxLTTB algorithm selects points for each bucket, highlighting the next average point, the preselected points, partition boundaries, and bucket boundaries.

cargo run -p example_minmaxlttb_analysis --release -- --show-buckets=on --show-partitions=on --show-min-max=on --show-selected=on --show-next-vertices=on --ratio=4

License

MIT